Research on Risk Assessment Model for Brushing Type Telecom Network Fraud Victim Based on Bayesian Network

Abstract

Assessing the risk level and exploring the risk characteristics of victims of brushing type telecom network fraud is of practical significance for crime risk prevention and control. Starting from the demographic characteristics of victims and case characteristics, this paper establishes a Bayesian network model with a tuple composed of loss amount and contact duration as the evaluation index of victim risk level, aiming to provide ideas for police to implement precise anti-fraud propaganda. The research shows that the tuple victim risk assessment model has a high prediction accuracy and can take into account both the loss amount and contact duration, which is feasible as a victim risk assessment model; there is no significant single influencing factor characteristic that affects the victim risk level; among the key victim groups of women who are commerce service personnel and use social media platforms, police should focus on highly educated groups, and among people with the same educational background, police should focus on young people under 28 years old.